The MORPH-II dataset has several key features that make it a valuable resource for researchers:
The true value of a verified benchmark lies in its ability to accurately measure progress. Over the years, algorithms evaluated on Morph II have achieved remarkable improvements.
Before diving into verification, let’s establish the baseline. The MORPH (Longitudinal Morphing) dataset, specifically Album 2 (commonly called MORPH II), was compiled by Karl Ricanek and his team at the University of North Carolina Wilmington. It remains the largest publicly available dataset of its kind designed for facial age progression and estimation. morph ii dataset verified
Because many subjects were arrested or photographed multiple times over those five years, MORPH II provides computer vision models with real-world, incremental data on human age progression. arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
: Research teams have published specific strategies for verifying the data, such as the MORPH-II: Inconsistencies and Cleaning Whitepaper , which highlights the necessity of correcting these errors before use. The MORPH-II dataset has several key features that
: Consists of approximately 55,134 unique mugshots .
The standard, non-commercial release of MORPH II contains a massive volume of real-world imagery. It functions as a "longitudinal" dataset, meaning it tracks the same individuals over a prolonged timeline. Dataset Composition and Demographics arXiv:2007
The hallmark of MORPH II is its longitudinal nature. It contains over of approximately 13,000 individuals taken over multiple years.
Iterative evaluation where one person's entire image series is held out. Eliminates identity leakage across subsets. Demographic Bias Mitigation
The original official application form was hosted at for academic use. However, some users have reported that the original website links no longer work reliably, so alternative sources may be necessary.
[1811.06446] Preliminary Studies on a Large Face Database - arXiv